Jasmine: Harnessing Diffusion Prior for Self-supervised Depth Estimation
This addresses the challenge of enhancing depth estimation accuracy without supervision, which is incremental as it builds on existing diffusion and self-supervised methods.
The paper tackles the problem of self-supervised monocular depth estimation by proposing Jasmine, a framework that uses Stable Diffusion priors to improve prediction sharpness and generalization, achieving state-of-the-art performance on the KITTI benchmark and superior zero-shot generalization across datasets.
In this paper, we propose Jasmine, the first Stable Diffusion (SD)-based self-supervised framework for monocular depth estimation, which effectively harnesses SD's visual priors to enhance the sharpness and generalization of unsupervised prediction. Previous SD-based methods are all supervised since adapting diffusion models for dense prediction requires high-precision supervision. In contrast, self-supervised reprojection suffers from inherent challenges (e.g., occlusions, texture-less regions, illumination variance), and the predictions exhibit blurs and artifacts that severely compromise SD's latent priors. To resolve this, we construct a novel surrogate task of hybrid image reconstruction. Without any additional supervision, it preserves the detail priors of SD models by reconstructing the images themselves while preventing depth estimation from degradation. Furthermore, to address the inherent misalignment between SD's scale and shift invariant estimation and self-supervised scale-invariant depth estimation, we build the Scale-Shift GRU. It not only bridges this distribution gap but also isolates the fine-grained texture of SD output against the interference of reprojection loss. Extensive experiments demonstrate that Jasmine achieves SoTA performance on the KITTI benchmark and exhibits superior zero-shot generalization across multiple datasets.